forked from mindspore-Ecosystem/mindspore
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_bprop """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common import Tensor
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from mindspore.common.api import jit
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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from ....mindspore_test_framework.utils.bprop_util import bprop
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE)
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
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@jit
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def construct(self, x, y):
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x = x * self.z
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out = self.matmul(x, y)
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return x, out
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def test_bprop_no_sens():
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grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)),
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Tensor(np.ones([3, 2]).astype(np.float32)), wrt=['inputs'])
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print(grads)
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def test_bprop_sens():
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grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
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grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32))), wrt=['inputs'])
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print(grads)
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def test_bprop_first_only():
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grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
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grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32))))
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print(grads)
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def test_bprop_wrt_params():
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net = Net()
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grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
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grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32))),
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wrt=['params'],
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params=net.trainable_params())
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print(grads)
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def test_bprop_wrt_params_no_sens():
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net = Net()
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grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
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wrt=['params'],
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params=net.trainable_params())
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print(grads)
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def test_bprop_wrt_inputs_and_params():
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net = Net()
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grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
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grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32))),
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wrt=['inputs', 'params'],
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params=net.trainable_params())
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print(grads)
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